{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年2月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developer Salary in First-Tier Cities 程序员工资调查\n",
    "我在4月1日到3日之间，抓取了某招聘网站的软件和互联网类招聘数据40万条，其中通过程序判断为程序员的14万条。地域方面，我选择了24个主要城市。不过本文只以一线城市为研究对象。这样是为了和我2017年6月的数据做对比。\n",
    "\n",
    "提到2017年的文章，现在居然还有很多人，把这篇文章拿出来炒作。对于社会来说，可气的是，他们直接把2017改成2019，就发表了，这不是骗人么？！对于我来说，可气的是，他们转载居然还冒充是原创，是可忍熟不可忍！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "sys.path.append('../py')\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "import weighted\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()\n",
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and city in ('北京','上海','广州','深圳')  and monthly_salary<80000\", con=conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_job_ids=['104660258','104142922','108434795','101357291','106253516','110368302','111391233','108665401','109277048'\n",
    "                  ,'73857191','108584955','102824950','102824949','111391233','110884556']\n",
    "data=data_original[~data_original.job_id.isin(error_job_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del data['publish_date']\n",
    "#del data['published_on_weekend']\n",
    "#del data['title']\n",
    "#del data['title']\n",
    "#del data['company_title']\n",
    "#del data['company_description']\n",
    "#del data['job_description']\n",
    "#del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_id</th>\n",
       "      <th>monthly_salary</th>\n",
       "      <th>headcount</th>\n",
       "      <th>title</th>\n",
       "      <th>zhinengleibie</th>\n",
       "      <th>career</th>\n",
       "      <th>real_province</th>\n",
       "      <th>province</th>\n",
       "      <th>city</th>\n",
       "      <th>company_description</th>\n",
       "      <th>...</th>\n",
       "      <th>pl_visual_basic</th>\n",
       "      <th>publish_date</th>\n",
       "      <th>published_on_weekend</th>\n",
       "      <th>tag_baby_care</th>\n",
       "      <th>tag_five_insurance</th>\n",
       "      <th>tag_flexible</th>\n",
       "      <th>tag_no_overtime</th>\n",
       "      <th>tag_rest_one_day</th>\n",
       "      <th>tag_rest_two_days</th>\n",
       "      <th>tag_stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>23076</td>\n",
       "      <td>118720034</td>\n",
       "      <td>17500</td>\n",
       "      <td>222</td>\n",
       "      <td>高级后台开发工程师/Java开发工程师（北京）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>营销实验室Convertlab（上海欣兆阳信息科技有限公司）为企业提供创新的数字营销平台DM...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-01-19</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16053</td>\n",
       "      <td>116717167</td>\n",
       "      <td>10000</td>\n",
       "      <td>200</td>\n",
       "      <td>海外业务软件工程师（2020届）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16321</td>\n",
       "      <td>116813374</td>\n",
       "      <td>12916</td>\n",
       "      <td>200</td>\n",
       "      <td>Java开发工程师（2020届）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>None</td>\n",
       "      <td>广东</td>\n",
       "      <td>广州</td>\n",
       "      <td>广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10953</td>\n",
       "      <td>114568087</td>\n",
       "      <td>3750</td>\n",
       "      <td>151</td>\n",
       "      <td>C#软件开发工程师（雄安新区岗位）</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3646</td>\n",
       "      <td>108834444</td>\n",
       "      <td>15000</td>\n",
       "      <td>150</td>\n",
       "      <td>Java开发工程师</td>\n",
       "      <td>软件工程师</td>\n",
       "      <td>一般程序员</td>\n",
       "      <td>None</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          job_id  monthly_salary  headcount                    title  \\\n",
       "23076  118720034           17500        222  高级后台开发工程师/Java开发工程师（北京）   \n",
       "16053  116717167           10000        200         海外业务软件工程师（2020届）   \n",
       "16321  116813374           12916        200         Java开发工程师（2020届）   \n",
       "10953  114568087            3750        151        C#软件开发工程师（雄安新区岗位）   \n",
       "3646   108834444           15000        150                Java开发工程师   \n",
       "\n",
       "      zhinengleibie career real_province province city  \\\n",
       "23076         软件工程师  一般程序员          None       北京   北京   \n",
       "16053         软件工程师  一般程序员          None       上海   上海   \n",
       "16321         软件工程师  算法工程师          None       广东   广州   \n",
       "10953         软件工程师  一般程序员          None       北京   北京   \n",
       "3646          软件工程师  一般程序员          None       北京   北京   \n",
       "\n",
       "                                     company_description  ... pl_visual_basic  \\\n",
       "23076  营销实验室Convertlab（上海欣兆阳信息科技有限公司）为企业提供创新的数字营销平台DM...  ...           False   \n",
       "16053  上海海隆软件有限公司成立于1989年4月，是国内著名软件企业、国家规划布局内重点软件企业。公...  ...           False   \n",
       "16321  广州汇智通信技术有限公司是专业从事国家特殊通信系统研制工作的大型国有控股混合所有制企业。公司...  ...           False   \n",
       "10953  北京其然科技有限公司，名字源于，顺 其其自 然然，主旨顺其自然、水到渠成。北京其然科技有限公...  ...           False   \n",
       "3646   大连华信计算机技术股份有限公司（简称大连华信）是一家面向全球客户提供领先的应用软件产品、信息...  ...           False   \n",
       "\n",
       "      publish_date published_on_weekend tag_baby_care  tag_five_insurance  \\\n",
       "23076   2019-01-19                 True         False                True   \n",
       "16053   2019-01-22                False         False                True   \n",
       "16321   2019-12-03                False         False                True   \n",
       "10953   2019-01-31                False         False               False   \n",
       "3646    2019-01-22                False         False                True   \n",
       "\n",
       "       tag_flexible  tag_no_overtime  tag_rest_one_day  tag_rest_two_days  \\\n",
       "23076          True            False             False              False   \n",
       "16053          True            False             False              False   \n",
       "16321         False            False             False              False   \n",
       "10953         False            False             False              False   \n",
       "3646          False            False             False              False   \n",
       "\n",
       "       tag_stock  \n",
       "23076       True  \n",
       "16053      False  \n",
       "16321      False  \n",
       "10953      False  \n",
       "3646       False  \n",
       "\n",
       "[5 rows x 94 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data.sort_values(by='headcount', ascending=False)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pd_weighted_mean(group, avg_name, weight_name):\n",
    "    \"\"\" http://stackoverflow.com/questions/10951341/pandas-dataframe-aggregate-function-using-multiple-columns\n",
    "    In rare instance, we may not have weights, so just return the mean. Customize this if your business case\n",
    "    should return otherwise.\n",
    "    \"\"\"\n",
    "    d = group[avg_name]\n",
    "    w = group[weight_name]\n",
    "    try:\n",
    "        return (d * w).sum() / w.sum()\n",
    "    except ZeroDivisionError:\n",
    "        return d.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unilateral Stats 总体统计"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有数据可知，程序员向一线城市集中的趋势非常明显。\n",
    "\n",
    "According to the statistics, significant amount of developers are in the first tier cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34786, 94)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111001"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5000., 11500., 15000., 20000., 37500.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_mean=weighted.weighted_mean(data.monthly_salary.values, data.headcount.values)\n",
    "q=weighted.weighted_quantile(data.monthly_salary.values,[0.025,0.25,0.5,0.75,0.975], data.headcount.values)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年2月中国一线城市程序员的平均工资为16457元，工资中位数为15000元，其中95%的人的工资位于5000到37500元之间。\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月中国一线城市程序员的平均工资为{salary_mean:.0f}元，工资中位数为{q[2]:.0f}元，其中95%的人的工资位于{q[0]:.0f}到{q[4]:.0f}元之间。')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2020, Developers in China earn 16457 Yuan as average, the median is 15000 Yuan, 95% of them earn between 5000 and 37500 Yuan.\n"
     ]
    }
   ],
   "source": [
    "print(f'In {year}, Developers in China earn {salary_mean:.0f} Yuan as average, '\n",
    "      f'the median is {q[2]:.0f} Yuan, 95% of them earn between {q[0]:.0f} and {q[4]:.0f} Yuan.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25718892e88>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.monthly_salary.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It does not look like normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=10201.604120570897, pvalue=0.0)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.normaltest(data.monthly_salary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "null hypothesis: x comes from a normal distribution\n",
    "    \n",
    "p=0\n",
    "\n",
    "The null hypothesis can be rejected\n",
    "\n",
    "conclusion: data is not normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zoom in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data.monthly_salary<40000].monthly_salary.hist()\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0,0), xytext=(2000, 100), color='white')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Role 角色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Common Functions\n",
    "def get_sub_stats_by_col(data, col):\n",
    "    categories=data[col].unique()\n",
    "    salary_mean=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    salary_median=[]\n",
    "    \n",
    "\n",
    "    count=[]\n",
    "    \n",
    "    categorys_out=[]\n",
    "    for category in categories:\n",
    "        #print(feature)\n",
    "        idata=data[data[col]==category]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(np.average(values, weights=weights))\n",
    "        \n",
    "\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        categorys_out.append(category)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data[col]=[c for c in categorys_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "\n",
    "    return sub_data\n",
    "\n",
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "    {\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0460d836_4684_11ea_89cc_701ce71031ef\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >career</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_0460d836_4684_11ea_89cc_701ce71031eflevel0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow0_col0\" class=\"data row0 col0\" >系统架构师</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow0_col1\" class=\"data row0 col1\" >25521</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow0_col2\" class=\"data row0 col2\" >5937.75</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow0_col3\" class=\"data row0 col3\" >25000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow0_col5\" class=\"data row0 col5\" >4938</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow0_col6\" class=\"data row0 col6\" >4.45%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0460d836_4684_11ea_89cc_701ce71031eflevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow1_col0\" class=\"data row1 col0\" >算法工程师</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow1_col1\" class=\"data row1 col1\" >21428</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow1_col2\" class=\"data row1 col2\" >5390.28</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow1_col4\" class=\"data row1 col4\" >45833</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow1_col5\" class=\"data row1 col5\" >12321</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow1_col6\" class=\"data row1 col6\" >11.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0460d836_4684_11ea_89cc_701ce71031eflevel0_row2\" class=\"row_heading level0 row2\" >0</th>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow2_col0\" class=\"data row2 col0\" >一般程序员</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow2_col1\" class=\"data row2 col1\" >15332</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow2_col2\" class=\"data row2 col2\" >5000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow2_col3\" class=\"data row2 col3\" >14000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow2_col4\" class=\"data row2 col4\" >35000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow2_col5\" class=\"data row2 col5\" >93555</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow2_col6\" class=\"data row2 col6\" >84.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0460d836_4684_11ea_89cc_701ce71031eflevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow3_col0\" class=\"data row3 col0\" >爬虫工程师</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow3_col1\" class=\"data row3 col1\" >12667</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow3_col2\" class=\"data row3 col2\" >2635.71</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow3_col4\" class=\"data row3 col4\" >27333.3</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow3_col5\" class=\"data row3 col5\" >162</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow3_col6\" class=\"data row3 col6\" >0.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_0460d836_4684_11ea_89cc_701ce71031eflevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow4_col0\" class=\"data row4 col0\" >生物信息工程师</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow4_col1\" class=\"data row4 col1\" >11300</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow4_col2\" class=\"data row4 col2\" >7000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow4_col3\" class=\"data row4 col3\" >12714</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow4_col4\" class=\"data row4 col4\" >15000</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow4_col5\" class=\"data row4 col5\" >25</td>\n",
       "                        <td id=\"T_0460d836_4684_11ea_89cc_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.02%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x257189f8f08>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career = get_sub_stats_by_col(data,'career')\n",
    "data_career.style.format({\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16457.073305645896"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data.monthly_salary * data.headcount) / data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111001"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    34786.000000\n",
       "mean         3.190968\n",
       "std          4.001360\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          5.000000\n",
       "max        222.000000\n",
       "Name: headcount, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns=100\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111001"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.headcount.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 城市"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "headcount_by_cities = data.groupby(by='city').headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "上海    36382\n",
       "北京    20529\n",
       "广州    23149\n",
       "深圳    30941\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headcount_by_cities\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iPnF6W0vT9a5DVKNqGAn2AwMb/fqaccABwP6Dt+eB5zb68wHADmXMdTUqQGcui1/c5zpD2BS619M9/wkKGzpK9zW72rGFga3fUbbm/Mbm1p1ch6hGMdcBNscYE7XWFob4+Hi883Dfstaeb4y5Hmjc5G7d1toTyxYukzgNOK5sX1+2aoJZf8D7Ig89fnvxCF2VWwGFnk5W3vYD6nc/hPZ/XsPkj5zD6jsvA1vExGuZ9IHvYqLxIT9v1V9/RnFDBzU77sGEE77Musf+Stcz/2THU8+je/7jjN7nXQ6eUeCMBy4HPug6SLXx5eHQwXN7fwIKQBI4EjgK+BDe0kEXW2tvH7zvXGvt4Zt8/sPW2sPKEi6T2BHIobVBneuydbnZvdfuDUaXiZdZz8IsJhqnduretP/zWqJjJxKfsCv1Mw5g9Z2XUb/bwYya+dYfuXWP/oVI/RhGz34HK//yM8YecjKdT/6dmp1mEt9hKoXu9TTsdaSDZxRYp7S1NN3iOkQ18eXhUGtth7X2HdbadwN/3+ivcsDxrxXgoOhQX6KM8S5FBegLDaYneXr0Di2uXQF1u6aonbo3PYueoXfZC4xOHUf9jAMAKG7oIDpq6GvSIvVj6F+1gGJPJ4X1K4mNnYi1FlsYoHv+E9TvdlAln0YYXNLY3DrRdYhq4ssS3IIX7VuHrs8aY+4zxjxljFlgjLkPaC/Lo2cSHwQ+UpavLcPyP7Gbd4qik0qVYK2lK/cvInWjIeK99+xdkqPY20nt1L2H/Jy6XWYz0LGSdY/9lfiEaUTqxlA/4wC6X36E2JiJrPz9D+lZ8HQln0bQTQJ+5DpENfF1CRpjpgBTh/j4xucynwCuw9vA9kbgCt64QrR0MokGtHCt79SYgcZvxn6njXcrwBjDhPd8gZpJjXS/9DCF7vWsuecqJpz4tc1+ztoHf8OE47/EuCNPJb7DLnRm76Eh+XbGHflxInUN1O9+CBteeLCCzyIUzmhsbt3fdYhq4csSNMYcZ4yZC7TwxqHHArDz4O/PMMacZYz5H+BY4LcbffrtwIeMMRcaY0r5/L7DEIUs7n0u+teZdfR2u84RZB1zb6PzmX8AUOztIlLbwKo/n8e4t6eJJSZv9vOKPZ30rWzDFgv0Lnvh9Y/3ty8lNm4nTDSOH69LqHIRtBP9NvNlCQIPAMdYa08DXsJbkeUB4IjBw53vxxv1TQA+ird/39eB5dbaLuDDwFKgtiRpMoldgG+V5GtJyUWNnXJu7Pp5rnME2ej9T6DrmXtZ/uvvYm2RgY4V9C1/mY6HbmH5b5rpyj1A36qFtD9w45s+L3H4R1lzx6UsuvBjFLvX0zDrGIq9G4g2jCc+YRrrn7qD+ukatJTB0Y3NrR9zHaIa+PLq0O1ljJkATAeeGOKc4chlEjcAnyr515WSKVrW7t97tVnH6EqvGiTiVwuBvdtamnSUZAv8OhLcLtba1dbax8tUgAcDnyz515WSihjGXRC/4knXOUR8ZFe8I2SyBYEowTI7H9A8tCrwzsgTh0xhzQrXOUR85LuNza2TXIfwM5XglmQS78dbs1SqgDGMurzmwhe2fk+R0BgLnOM6hJ8F4pxgWXjbJD0JpFxHkW1nLQPv7vvZkpft1Omus4j4xAAwu62lSW8Qh6CR4OZ9AhVg1TGG2FXx85e4ziHiIzG86WYyBJXgUDKJGuBc1zFkeHY3y4440LyQd51DxEdObmxu3dd1CD9SCQ7t87x1ZwqpEsZgrqi5sMt1DhEfMcBZrkP4kUpwU5lEPfpmqXo7mrUHHRd5VFMmRN7wkcbm1r1ch/AbleBbpYHNrwMlVeMX8St8u1+miAMR4HuuQ/iNSnBj3hWh33QdQ0pjrOne59ToP7ScmsgbPt7Y3DrDdQg/UQm+2cnAHq5DSOmcE7thgqFYdJ1DxCdiwHddh/ATleCbfdt1ACmtOtO/+5ejf9JWSyJvOK2xuVU74gxSCb4mkzgKONx1DCm9r8b+ML2G/l7XOUR8oha94X+dSvAN+qYIqJgp7nJW7KaHXecQ8ZEzG5tbdQEgKkFPJrE33h6FElCfit4zq4Hu9a5ziPjEKLTDBKASfM030U4RgRYxduJP41c/7jqHiI+c2djcWpqNx6uYSjCTmII2zA2F90YePnACHatc5xDxiQnAR12HcE0lCF/BO1EsAWcMYy6rufg51zlEfOTzrgO4Fu6tlDKJBmARMN51FKkMa+k7pu+CVxfaHXdxnUXEJ1JtLU3PuA7hSthHgh9DBRgqxlBzVfz8Ba5ziPhIqEeDYS/BtOsAUnl7m0VHzDbzX3KdQ8QnPtXY3NrgOoQr4S3BTGIGcLTrGFJ5xhC5quaCNa5ziPjEWOBU1yFcCW8JwqfRtIjQ2sWsOvSoSDbrOoeIT4T2kGg4SzCTMHglKCF2SfwSLawt4jmosbn1YNchXAhnCcJRwG6uQ4hb403nfidH/v2o6xwiPhHK0WBYS1AXxAgAP45fOwbCPE9I5HUfa2xurXMdotLCV4KZRD1aJUEGjTK9e50R/Zu2WhKB0cCJrkNUWvhKED6IdzWUCADfjf12aoyBftc5RHwgdAOEMJagDoXKm8RNYfp3YrdoNCgC7w/bIdFwlWAmMRV4t+sY4j+fif5t73p6N7jOIeJY6A6JhqsE4ROE7zmXzYrOIv2FYFxTEjV28o/i1z7iOoeID4TqkGjYCuFk1wFcWdFZ5OjrugB4pb3Iu27oYv8rO/nh/b2b/Zwl64rscv56jr2+i2Ov72JlV5FL5/VxyC876eqz3PnyAPFocNYb+GDk3/uPY3276xwijoXqkGh4SjCTmAAc5jqGC+3dlvSfuunq80Ztl87r49xja3ny86O58+UBVnYNPWf84SUFzjq6lvtOa+C+0xqY1BDhyeUFzjighkeWFmiIB6cAAYwhcWH8sqdd5xBxbDRwgusQlRKeEvT+U8P0fF8XjcAtHxnF2FqvtCbUG55eUWRFZ5HegmVc3dBlNndxgWue6OPAqzr53j96AG9GXX8R7np5gBNnxir2HCrlmMjTh+7MqmWuc4g4FppDomEqhSbXAVwZW2tIbFR0J+wRY+6SAhc/3Mc7G2PENvNdcOIeMe5LN/DImQ08tLjA0ysKvGf3GLe/0M8uYyOcdPMG7p0/UKFnURnGUH9FzYXaYULCLjSHRMNRgplEFDjedQy/aHmwl+s/UMeP3lVH9wDc/UphyPu9bVqUMbWGaMRwwJQoL64u8rF94mSOrWVcnaFpZozf54I3vW5f88oRe5pF813nEHFoDCF5zQxHCcLhwA6uQ/jF/PYii9ZZegYsjy8rbHYrjeNv2sCy9UU29FvuenmAfSZ73y4vri6y+3hDbcxQDMbFoW9iDLGr4hcsd51DxLFQnBcMSwm+13UAP/nBsbUce30Xk362nmkJwztnRPnn/AEundf3pvudc0wt75izgcOv6eLzB9ew18Qo63otU0ZHmDUpytWP9fHu3YJ3XhCg0Sw//BCTz7nOIeLQu1wHqARjw7B2cCbxJLCf6xhSXVbaxOOH9F5xoOscIg5Nb2tpWug6RDkFfyTorRKjApTtNsl0HHh8ZN4TrnOIOBT40WDwS1CHQmUEfhG/stZ1BhGHVIIBoBKUYRttemZ9Knr3XNc5RBwJfAkG+5xgJlEDrMZbAUFkWHpt7JVk7/XTi0SirrOIOLBPW0vTs65DlEvQR4IHoAKUEao1A7t9NfZ7bbUkYRXonXeCXoKhXCtUSu9L0T/vVktfj+scIg4E+pCoSlBkG8RMcefvx2542HUOEQeOaWxuDeaEYIJfgoe6DiDBcWr03tRoNqxznUOkwsYCB7kOUS7BLUFv66Q9XMeQ4IgYu8PP41dq3qCE0cGuA5RLcEtQo0Apg+Mjjx48kbUrXecQqbDArpykEhTZDsbQcHnNRVpTVMJGJViFdFGMlMUh5vnDG82yRa5ziFTQ7Mbm1hrXIcohyCWokaCUhTHUXB0/XyUoYRIHUq5DlEMwSzCT2AOY4DqGBNdMs+Tw/cxLL7jOIVJBgbxCNJglqFGglJkxRK6subDDdQ6RCgrkecGglqDOB0rZ7WTWHHJs5MmnXecQqRCVYBXZ33UACYeL4pcZ1xlEKiQVxJVjglqCmiQvFZEwXamPRO9/xHUOkQqoA2a5DlFqwSvBTGIUsJPrGBIeP4xdN85QLLrOIVIB+7kOUGrBK0HYHdAhKqmYetM383PR27XxroTB7q4DlFpQS1Ckor4Z+920GAP9rnOIlNkM1wFKLYglqPOBUnFxU5jWHLtZG+9K0O3mOkCpqQRFSuT06B3JUfR0uc4hUkYaCVYBHQ4VJ6LGTjov/stHXecQKaOdG5tba12HKKUglqBGguLMSZGHDhjPujWuc4iUiQGmuw5RSsEqwUyiBpjmOoaElzGMvTh+adZ1DpEyCtR5wWCVIDQCUdchJNyOijxz2C5m5VLXOUTKJFDnBYNWgjoUKs4ZQ92V8QtecZ1DpExUgj4WqGG6VK/Zpu2Ivc1CFaEEkUrQxya5DiACYAzRq+Lnv+o6h0gZNLoOUEpBK8HxrgOIvGZ65NXDDzPPPec6h0iJBWrD8qCV4DjXAUQ2dnnNRb2uM4iUWKAGG0ErwUD950j1m2DWH9AUmfu46xwiJTS2sbk1MJsUqARFyuyn8avrwVrXOURKJAIkXIcoFZWgSJk1mJ7kadE7tdWSBElgTj2pBEUq4Hux30yJUhhwnUOkRALzWhu0EgzMuxMJlhozMOMbsdu01ZIERWBea4NTgplELVDvOobI5nw++tc96ujtdp1DpAQ0EvShwPynSDBFTXGnH8TmzHOdQ6QENBL0IZWg+N5Ho/ftN5bODtc5REYoMK+3KkGRCooYxp0fv/JJ1zlERkgjQR9qcB1AZFu8K/L4ITuyRuuKSjWrcR2gVIJUgpqMLFXBGEZdXnNR3nUOkREIzL6tQSrBousAItvqQPPiEbuZpQtc5xAZJpWgD2kkKFXDGOJXx89f4jqHyDCpBH1II0GpKrubpUccYF583nUOkWEITAnGXAcoIZWg+I4F2xGJdKyKRtuXx6Kdy6PRDctisf7lsejAq7GoOfKpG9o/9+jY1a5zimyP9toxq6DJdYySUAmKbIc+6F0Vi655NRpdtzwW61wWi/Yuj8b6V8SirIxGTXs0UrMuEqnrNpEx/YaxFnbAmHFs5pLy41/qvn/6+u63VfhpiIzI9PUrnnCdoVSCVII6JyjbZWujtNXRaLQjEqnrikRG9RgztgDjMGY0sNPgbcR2XRWcLWkkVAIz6AhSCQbmP0WGZ1tGaesjkfpuYxr6jBlnvVLb7CitEkZ3M8PVY4uMQMF1gFJRCYov+WGUVm6T19qlBnZ2nUNkGALzehukEtThUB/rg95V0Wj7yli0Y3ks1rUsGu1ZFosNrIhFiyuj0Wh7NBJbH4mM8tMordxmLbSLUQlKdep1HaBUglSCgXln4nfbM0rrNWbMAIwfHKVNGbwJkGqzG1xnEBmmda4DlEqQSjAwx6grTaM0N/ZYamtdZxAZJpWgD+ldNW8dpa2IRruXxWK9y2PR4quxKKui0VhHJFLTFYk0aJTm1sR1+veWqqUS9KE1rgOUg0ZpwVTTbzfECkx3nUNkmFSCPrTWdYCtsWDXRSIdK6PRtSti0fXLNUoLrZlL7XwDs13nEBkmlaDvZDoGyCTWA2Mq9ZDbOUpLWK/UNEoTUm02kEcuJDRUgj61hmGWoEZpUknJhVZTeqSaqQR9qh288ywbjdLWrYhGO5fFYj3LYtH+FbEYK6NRs8ZbPaSu25jRGqVJpe2i5dKkuqkE/eid03Z+pj0anaBRmvjd6B4aXWcQGYEO1wFKJVAluDIWM8A01zlEtmTHNXaxgV1c5xAZpvZkPheYFWOCtKkuwHLXAUS2ZvZCu9R1BpERWOw6QCmpBEUqbJ8FWi5Nqtoi1wFKSSUoUmF7LLV1rjOIjIBGgj6mEhTfm7iuOrZ6EtkMlaCPqQTF12r7bFe0yK6uc4iMgErQxwL1nyPBs+cSO9+AcZ1DZAQC9TobqBLMprNrgRWuc4hsjpZLkwBQCfrcc64DiGxOcpFWS5Oqp6tDfU4lKL61yyrGu84gMgKrkvlcp+sQpaQSFKkUa+2oXi2XJtgFLLwAABkASURBVFXtWdcBSk0lKFIhO61hkangVl8iZRC411eVoEiFzF6g5dKk6gXu9TVwJZhNZ18FVrnOIbKp1AIbmEWHJbR0OLRK5FwHENnUbstsresMIiOkkWCVCNx/lFS/CevZ2XUGkRFYncznAjcPWyUoUgH1vXZ9tKi9LqWqBfJ1VSUoUgEzl9g2LZcmVS5w5wNBJShSEfu22XbXGURG6BnXAcohkCWYTWeXAh2uc4i8RsulSQA87DpAOQSyBAdlXQcQec3Oq9nBdQaREegGnnIdohyCXIL/ch1ABNByaRIEjybzuX7XIcohyCV4v+sAIgBTV7PQwGjXOURG4CHXAcolyCX4b2DAdQiR2QvsMtcZREZIJVhtsulsF/Co6xwiWi5NAkAlWKXucx1AZLdlts51BpERmB/ElWJeE/QS1HlBcW58J1NdZxAZgcCOAiH4JajzguLUqB7bESuyi+scIiOgEqxW2XS2E3jMdQ4Jr70W2wWuM4iM0D9cByinQJfgIB0SFWdSbXat6wwiI7Agmc8Femu6MJTgfa4DSHjtvdhq0WypZne6DlBuYShBnRcUZ7RcmlS5O1wHKLfAl2A2nV0PPO46h4SPsbZY38cM1zlEhqmfgJ8PhBCU4CCdF5SKm7qKBQZGuc4hMkwPJfO5da5DlFtYSvAu1wEkfPZZYJe7ziAyAoE/FArhKcF7gZWuQ0i4pNq0XJpUNZVgUGTT2QLwB9c5JFxmLLcNrjOIDNMK4EnXISohFCU46BbXASRcxneys+sMIsP0p2Q+Z12HqIQwleD9eO9uRMpudLddG7VaM1SqVmgGDaEpwWw6WwRuc51DwmGvxbbNdQaRYVpOiK6oD00JDrrVdQAJh1SbDfyl5RJYtyXzuaLrEJUSthL8N7DUdQgJPi2XJlUsNIdCIWQlqEOiUik7rWGC6wwiw7AYeNB1iEoKVQkOCtW7HKm8SNEW6vrYzXUOkWG4NSxXhb4mjCX4ELDIdQgJrmkrWWCgznUOkWEI3SAhdCWYTWct8DvXOSS4Zmu5NKlO85P53DzXISotdCU4KHTvdqRy9llg+1xnEBmGOa4DuBDKEsyms/OA+a5zSDBpuTSpQgXgGtchXAhlCQ66ynUACaZxXUxznUFkO7Um87klrkO4EOYS/CXQ7TqEBMvoDbY9apniOofIdrrSdQBXQluC2XR2DXCT6xwSLMlFWi5Nqs4C4E7XIVwJbQkOuth1gDAa6BjADgRzKpKWS5Mq9MswLZO2qZjrAC5l09lnUnNS/wTe6TqLHxU2FFh0xSJs0RKpjTDti9NYOmcpvUt7GbPfGCafNHnIz+tb2cfSG5dS7ClSP6OenU7didX3rGbtg2uZ0TyD9c+sZ/yR4yv8bCpj78U26jqDyHYYAH7lOoRLYR8JgkaDm7X2obVMPH4iM749g3giTsfDHVCE3c/enb6VffQuH3rj9OW3LmfySZPZ7Xu70d/eT2euk56FPYx/+3g2vLKBSE1wv+2mtGu5NKkqf0nmc8tch3ApuK9G2+6vwCuuQ/jRhHdNYPQ+owEYWD/A2v+sJXFoAoDRs0ez4cUNQ35e3/I+6hq9BVNiY2IUu4tYa7EFS+eznYzZd0xlnkCFRYp2oLZfy6VJVQn9VfKhL8HBRbUvc53Dzza8tIFCV4H4DnFi470j6NHRUQY6Boa8/9hDxrLyTytZ98Q6Op/ppGFWA2P2GcP6J9cTHx9nwUUL6Mx1VvIpVMT0V1lgoNZ1DpFt9Cxwt+sQroW+BAddCwTvVbkEBjoHWHrTUqZ+ZirRuii2z7ugpdjjje6GMvmkyYzedzTtD7Qz7shxROuiJA5LMPnkyURHRRmz3xjWPRq860dmL7ArXGcQ2Q4/D9ti2UNRCQLZdLYDuMF1Dr8pDhRZdNkipnxkCjUTa6hrrHv9EGjPoh5qJtZs9nPrdq2jf3U/E4+f+PrHelf0UjO5BhMzEMAfPS2XJlVkKfAb1yH8QCX4hosJ5Evz8LU/0E73gm5e/eurvHLeK2Bh7X/WsuzmZaybt44x+42hZ0kPK37/1gHQqr+vYsLxE4jUet9ihe4C8USc2p1rab+vnYZZwVtZrHGFHe06g8g2uiiZz+lNG2A2d0grjFJzUncAx7vO4WeFrgKdz3Yyas9RxMfFXcfxld+2DKyIWHZ0nUNkKzqAXZP5XPDOSQyDRoJvdqHrAH4XbYiSODShAtzE2C67WgUoVeJSFeAbVIIbyaazdwBzXeeQ6pNcZBe4zhAWqwYG6NcRrOHqQm/23yTUK8ZsRjNwn+sQUl1SbXa96wx+s75Q4JvLllK0lvpIhENHjeKe9d4/07pikX3r6vnBlLeuNb64r4//e3UFncUiqbo6vjt5R37d3s6f13Vw/bRdebCriw8kEpV+OkFxdTKfW+U6hJ9oJLiJbDp7P3CH6xxSXfZabPWGchO3r1vHaeN34JppuzIxGmNqPM6cXaczZ9fpHFRfz0fHDV1kv1i1ki9MmMhNu05nxcAA8zZ0ke/t4cOJBM/0dFMfMRV+JoHRDfzcdQi/UQkO7X/QlaKyHXZcy8St3ytcTh0/nrc1eFcBtxcKTIh67xNW9PezulBgn7r6IT+vra+PZJ234tAO0SjrC0UsMGDhwa4NHN2gi3CH6ZJkPrfUdQi/UQkOIZvOPgnc4jqHVIdowfbX9jPDdQ6/erK7m45igf3qvdL7zdq1nDJu3Gbvf/yYMVy+ahX3dq7n311dHN7QwJGjGri/s5MpsRhfWrKYhzd0VSp+ULQDLa5D+JFKcPP+F+h3HUL8r/FV2gxsfuWAEFtbKPCjFSv4vyk7AVC0lnkbuji0ftRmP+fzEyZydEMDt3V0cHIiQUMkwoljx/KliRMZE41wTMNo7l6vU7DbqSWZz7W7DuFHKsHNyKazL+MtpyayRfu02ZWuM/hRn7V8fekSvj5pElPj3pSax7q72be+HmO2fF5v77o6lvX3kx6/w+sfW9DXx7R4DTXGENrN74ZnMdotZ7NUglt2LjD0Vgkig2ZrubQh/WHtWnI9PVy1ehXphQv4+7p1/Luri4M3GgW+1NvLRSvf+h7iV2tWc9r4HaiPeC9RnYUCE2Mx9qit4daOtRwxKngrDpVRJpnP9bgO4VdaMWYrUnNSP8a7UEZkSFddPPDY+C4Ocp1DZAg5IJXM5wqug/iVRoJb9xNgjesQ4l+JDezqOoPIZpylAtwyleBWDO4w8RPXOcSfxnXaVRHLJNc5RIbwUDKf+6PrEH6nEtw2lwBLXIcQ/9FyaeJTReArrkNUA5XgNsims93ovKAMITXfajNm8aMrk/ncY65DVAOV4DbKprM3Ane7ziH+stcSLZcmvrMCOMt1iGqhEtw+n0NTJmQjk9cy2XUGkU18O5nPrXUdolqoBLdDNp2dD5ztOof4Q6xg+2oGaHSdQ2Qj9yfzuRtdh6gmKsHtdxHwiOsQ4l7jcuYb0O7C4hf9wBddh6g2KsHtlE1nC8AZwIDrLOJWSsulib+cn8znnnMdotqoBIchm84+DfzUdQ5xa/ZCq0nI4hcL8ZZ5lO2kEhy+c4HnXYcQd3Z91Y5xnUEEb+/TM5L5nC7aGwaV4DBl09le4LNo893QGqvl0sQfrkjmc5q+NUwqwRHIprMPAFe7ziGVt8M6uyKCdpMX514Evu06RDVTCY7cd4ClrkNIZSUX2cWuM0joFYBP6zDoyKgERyibzq5DlyWHTqrNamtzce0nyXxurusQ1U4lWALZdPbPwE2uc0jl7LnEan6guPQkkHEdIghUgqXzeUBzdEJicgc7us4godWLdxi033WQIFAJlkg2ne0CPgxoV4GAiw3Y3riWSxN3zk7mc1nXIYJCJVhC2XQ2jzdtQgJsN2+5NO0eIS60Aj93HSJIVIIllk1nbwaucJ1DyifVZle5ziCh1AZ8KpnPaW5yCakEy+NraJHtwNJyaeJAL/CRZD7X7jpI0KgEyyCbzvYB/wXoGzaAdn3VJlxnkND5b+0UXx4qwTLJprNtwKfRsmqBM7pby6VJRc1J5nNamapMVIJllE1nbwd+4jqHlM6EDrssAju4ziGh8TTwBdchgkwlWH7/C9znOoSUxqyFdonrDBIa6/DOA3a7DhJkKsEyG9yE91RguessMnKpBVbzQKUSinhXgr7oOkjQqQQrIJvOLgdOAbTCQ5WbucTWuM4gofCNZD73F9chwkAlWCHZdPZ+4EzXOWRkJmm5NCm/S5L53EWuQ4SFSrCCsunsHOD7rnPI8MQHbE+8oOXSpKxuB77uOkSYqAQrLJvO/hC4xnUO2X67L2O+gajrHBJYjwOnJPM5LcZQQSpBN74A3OE6hGyfVFtRy6VJuSwC3pfM57pcBwkblaAD2XR2APgo3js/qRKzFtii6wwSSOuBpmQ+t8x1kDBSCTqSTWc7gfcCL7nOIttm2irGuc4ggdOHNxdQWyM5ohJ0KJvOrgCOAzQBuwqM6Wa66wwSKAN45wDvch0kzFSCjg2uMfoeYLXjKLIFk9bapQaNBKVkini7w//RdZCwUwn6QDadfQ44Ee1K71uzF9rFrjNIYFjgjGQ+d7PrIKIS9I1sOvsIcDLevmHiM/u0Wa3fKKXy5WQ+d53rEOJRCfpINp39B/ARoMd1FnmzPZZquTQpiW8l87nLXYeQN6gEfWZw+6X3okOjvjJpHVNcZ5Cqd04yn/uF6xDyZirBjRhj9jPGHOU6RzadvRd4F7DGdRaBmn67IVbQlaEyIj9M5nPnug4hb6USfLNO4P+G+gtjzHeMMSds8rGoMeY/5QiSTWfnAW8HNIHWsZlLbZvRz4oMj8XbEUJrBvtU6H+wjTGfNcb82xhzH3AtUG+MuW/wNtcYc9LgXS2DF60YY+4FsNYWgLJdMJFNZ58FjgLml+sxZOv2abOaviLDUQA+k8znLnAdRDYv5jqAD4wDrrDW/nor97ODN4CuTT5eNtl09pXUnNRRwN3ArHI+lgxt1kItlybbrRf4eDKf+4PrILJloR8J4r1b2+LOAMaY2wd/+77XRobGmHONMWPKHQ4gm84uxTs0+mglHk/ebBctlybbpxNvMWwVYBVQCXoFuNnRnDEmCtQBBpiHt/A1eIX02bKnG5RNZ1cD7wTur9Rjimd0j/YQlG22Bnh3Mp+7x3UQ2TYqQZgAdGzh7xvxdnvYEXgOaB38eCsVXvMzm86uB07YKIOU2Y7tdrGBhOscUhWWAcck87mHXQeRbacShEOB/Ob+0lr7srX2O3jn45631v528OOF135fSdl0tgf4IFDxxw6jWQvtUtcZpCo8DRyWzOeecR1Etk+oS9AYcxiQsNa+sJX77Qmssdb2v/ahsofbgmw62w98Am86R1kvzAm7VJvd4DqD+F4rcGQyn1vkOohsv1CXIF6ZfX0b7ncB8KON/rwMXj9fOKoMubYqm84Ws+ns2cCH8DbllDLYY6mtc51BfO1i4APJfE4rPFUpY60GEltjjBllrX9HBKk5qb2BPwF7uc4SNL/+yUBbvKgLY+Qt+oEvJfO5X7oOIiMT9pHgNvFzAQJk09k83rnNP7vOEiS1fbYrVmRX1znEd1YC71IBBoNKMCCy6ew6vAtmvo+3YaeM0J5L7HwtlyabeAo4JJnP/ct1ECkN/YAHSDadtdl09ofA+4G1rvNUu1SbbXedQXzlWuCIZD63wHUQKR2VYABl09m/AYcAz7rOUs2Si3TCXABvBZhPJvO5M5L5nDZXDhiVYEBl09mXgMOA37nOUq2mark08Q5/HpzM57a2trBUKZVggGXT2a5sOvtfwHfx1kiVbWWtbehlhusY4tSVwOHJfO5510GkfFSCIZBNZ38KHAnkXGepFjutYbGBiiyQLr6zDvhYMp/7QjKf63EdRspLJRgS2XT2YeBA4KdoVLhVWi4ttB4FDkzmc7eW6wGMMUcbY5yuOiVvUAmGSDad7cmms9/F26hXh3i2INVmdQFEuPQC38M7/PlymR+rEbjRGHO3MeaeTW5LjDEzjTHvNsacB2CMucQY8/qheWPMP4wx2gu2RFSCIZRNZ+cC+wM/R3MKh7T7Mi2XFiLz8EZ/5yXzubIfJbHW3gicAwwAnwYuG/z1RmDO4Md78IoZYDTAYGlGgB5r7UC5c4aFSjCkBkeF3waOBra4gHgYTVjPzq4zSNn14F009rZkPvdcJR7QGPN+Y8zPgYXA54Dr8bZoWw0ch/ezOBo4BTjCGPN+oA/vFEavtbaIFs0vKZVgyGXT2f/gjQrPR6NCAOp77fpokWmuc0hZzQUOSOZzP63E6O811tq/Ao/gXah2GPA1YCJwHnAGsAFYDDyIV4hPADV4a5VKGei4spBNZ7uBb6bmpP4AXAfMdBzJqZlLbJuBlOscUhY9wNnA+cl8zsmbPmvtLYMXxvwAr/gmAJOBJN5r8lpgEbDaWrvYGLMj3oR9KQONBOV12XT2QWA/4EJCfAVpqs1qyblg+iOQTOZzP3dVgADGmFrgBmvt94GzgFq8kvuztfY4a+1dg3eNG2MmAg3WWm2XViYaCcqbDI4Kv56ak7oG+BlwouNIFafl0gLnOeCryXzuHtdBBn0K+I8xpg5vytLZeFdsn2KM+QewL/BFvFHr2/HOF4IGLWWhf1QZUjadfTabzr4XeA/wtOs8lTR1NeNdZ5CS6AC+AeznlwI0xsSB/wb+AvweuATvHOAAcCZwCzAfONla2wS8D/jV4Ke3DW7kHR28SlRKQP+QskXZdPZu4ADgM8Ayx3HKz1o7SsulVTuLVxx7JvO5C5L5nJ+mEzTild9q4BfW2j8CcaDeWvsi3hWiT1pr1w/OBfyTtfZ2AGvtF621Bbzz9lIi2lletllqTqoB+BbwbaDBcZyymLrKLrzglwVtpFu9HsI79PmI6yBSHVSCst1Sc1I7AT8ETidgRxOOe7w498w7i4e7ziHb7Ung7GQ+d7vrIFJdAvUCJpWRTWeXZdPZM/AOk97tOk8ppdqsFkyuLjngv/BWfFEBynbTSFBGLDUndQLeZN/9XWcZqcsuG3h40joOc51DtuplvHl2v3Y53UGqn0pQSiY1J/VuvPOF73GdZbh+85OBxbEiu7jOIZu1CO9Q/HU+u+BFqpRKUEouNSe1L94FNKfgXflWFep77LrrLyiMMaBtbvznGeAC4KZkPtfnOowEh0pQyiY1JzUV+CreQsFjHcfZqv1fLj79vVuL+7rOIW9yN/CLZD53p+sgEkwqQSm71JzUWLyJwF8D/x5q/PQ9hQfe94h9u+scQh/wG7z1PbOuw0iwqQSlYlJzUnHgY3iHSvdzHOctfnz9wL/2WMbRrnOE2BrgSuDSZD4X/IUZxBdUguJEak7qOOBLwHvxyXnD638x8OyoPma7zhEyFrgXb4WXPyTzuW7HeSRkVILiVGpOagLePK9PAm9zlcNYW/xtS6HHwChXGUJmEd6Gstcl87n5jrNIiKkExTdSc1K7AZ8YvO1VycfeZaWdf/41Ba0ZWl69wJ/xRn13a36f+IFKUHwpNSd1CN7o8BS8DUfL6oRHi3P/391aLq0MCsC/gNuAm5P53BrHeUTeRCUovpaak4rhraz/SeBkynS48lu/L9x36Av22HJ87RDqB/4B/AH4UzKfW+k4j8hmqQSlaqTmpEYDJwEn4BXjlFJ97csvG5g3cR2HlurrhVA3cCfeNkG3J/O5tY7ziGwTlaBUrdSc1D54ZXgccAwjGCXe3DKwJGqZWqpsIbEAbzL7HcAdyXyuy3Eeke2mEpRASM1J1eBdXfpaKR7ENu6S0tBtO667sJAoY7ygaAceAO4B7krmcy84ziMyYipBCaTUnNQOwDt5oxQ3e+XngS8Wn2q+rei7yfs+sBJ4GG8e373AU7qiU4JGJSihkJqTmo43OjwIOHDw10kAp91duP+9j9pjHMbzgw7g0cHbI8CjyXxugdtIIuWnEpTQSs1J7QIc+K3bCjMOfdEeDaSA3YGo22RlZYElwAvAU7xRei8l8zm9GEjoqARFNpLbO1kHJPEm688AGgdvM4BdgVpX2bZTO17RPT/462u3F5P53AaXwUT8RCUoso1yeycNsBNvlON0vEOqO2xymwCMB2IljtAPrAWWb3RbscmflwPLNCldZNuoBEXKJLd3cixeKY7GK8T44K+xIf4cBXqADZvcul/7vXZSFyk9laCIiITWNs2jEhERCSKVoIiIhJZKUEREQkslKCIioaUSFBGR0FIJiohIaKkERUQktFSCIiISWipBEREJLZWgiIiElkpQRERCSyUoIiKhpRIUEZHQUgmKiEhoqQRFRCS0VIIiIhJaKkEREQktlaCIiISWSlBEREJLJSgiIqGlEhQRkdBSCYqISGipBEVEJLRUgiIiEloqQRERCS2VoIiIhJZKUEREQkslKCIioaUSFBGR0FIJiohIaKkERUQktFSCIiISWipBEREJLZWgiIiElkpQRERCSyUoIiKhpRIUEZHQUgmKiEhoqQRFRCS0VIIiIhJaKkEREQktlaCIiISWSlBEREJLJSgiIqGlEhQRkdBSCYqISGipBEVEJLRUgiIiEloqQRERCa3/D2hfk/RcJ5orAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(headcount_by_cities.values, labels=headcount_by_cities.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_048bb8c6_4684_11ea_beb4_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >city</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow0_col0\" class=\"data row0 col0\" >北京</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow0_col1\" class=\"data row0 col1\" >18098</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow0_col2\" class=\"data row0 col2\" >3750</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow0_col3\" class=\"data row0 col3\" >15000</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow0_col4\" class=\"data row0 col4\" >45000</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow0_col5\" class=\"data row0 col5\" >20529</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow0_col6\" class=\"data row0 col6\" >18.49%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow1_col0\" class=\"data row1 col0\" >深圳</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow1_col1\" class=\"data row1 col1\" >16903</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow1_col2\" class=\"data row1 col2\" >5750</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow1_col3\" class=\"data row1 col3\" >15000</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow1_col4\" class=\"data row1 col4\" >37500</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow1_col5\" class=\"data row1 col5\" >30941</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow1_col6\" class=\"data row1 col6\" >27.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow2_col0\" class=\"data row2 col0\" >上海</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow2_col1\" class=\"data row2 col1\" >16768</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow2_col5\" class=\"data row2 col5\" >36382</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow2_col6\" class=\"data row2 col6\" >32.78%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow3_col0\" class=\"data row3 col0\" >广州</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow3_col1\" class=\"data row3 col1\" >13917</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow3_col2\" class=\"data row3 col2\" >3750</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow3_col4\" class=\"data row3 col4\" >30000</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow3_col5\" class=\"data row3 col5\" >23149</td>\n",
       "                        <td id=\"T_048bb8c6_4684_11ea_beb4_701ce71031efrow3_col6\" class=\"data row3 col6\" >20.85%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25714094288>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_col(data,'city')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_beijing=data[data.city=='北京'].monthly_salary\n",
    "salary_shanghai=data[data.city=='上海'].monthly_salary\n",
    "salary_guangzhou=data[data.city=='广州'].monthly_salary\n",
    "salary_shenzhen=data[data.city=='深圳'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=96.17821702319924, pvalue=1.204381699088008e-22)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_beijing, salary_shanghai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=12.739425910632669, pvalue=0.00035879084748709103)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shanghai, salary_shenzhen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=574.878981989291, pvalue=4.845021704224348e-125)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_shenzhen, salary_guangzhou)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编程语言"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_04a72ff8_4684_11ea_91b0_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col1\" class=\"data row0 col1\" >rust</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col2\" class=\"data row0 col2\" >25459</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col3\" class=\"data row0 col3\" >26500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col4\" class=\"data row0 col4\" >9281</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col5\" class=\"data row0 col5\" >46094</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col6\" class=\"data row0 col6\" >225</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col1\" class=\"data row1 col1\" >python</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col2\" class=\"data row1 col2\" >20819</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col4\" class=\"data row1 col4\" >5500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col5\" class=\"data row1 col5\" >45000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col6\" class=\"data row1 col6\" >14695</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow1_col7\" class=\"data row1 col7\" >9.46%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col1\" class=\"data row2 col1\" >go</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col2\" class=\"data row2 col2\" >20677</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col4\" class=\"data row2 col4\" >7000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col5\" class=\"data row2 col5\" >45000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col6\" class=\"data row2 col6\" >14205</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow2_col7\" class=\"data row2 col7\" >9.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col1\" class=\"data row3 col1\" >lua</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col2\" class=\"data row3 col2\" >20530</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col3\" class=\"data row3 col3\" >20000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col4\" class=\"data row3 col4\" >5693</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col5\" class=\"data row3 col5\" >41812</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col6\" class=\"data row3 col6\" >1479</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow3_col7\" class=\"data row3 col7\" >0.95%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col1\" class=\"data row4 col1\" >matlab</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col2\" class=\"data row4 col2\" >19947</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col4\" class=\"data row4 col4\" >5666</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col5\" class=\"data row4 col5\" >45000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col6\" class=\"data row4 col6\" >2454</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow4_col7\" class=\"data row4 col7\" >1.58%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col1\" class=\"data row5 col1\" >typescript</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col2\" class=\"data row5 col2\" >19263</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col3\" class=\"data row5 col3\" >22500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col4\" class=\"data row5 col4\" >3910</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col5\" class=\"data row5 col5\" >35000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col6\" class=\"data row5 col6\" >854</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.55%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col1\" class=\"data row6 col1\" >ruby</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col2\" class=\"data row6 col2\" >19255</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col4\" class=\"data row6 col4\" >3612</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col5\" class=\"data row6 col5\" >37500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col6\" class=\"data row6 col6\" >467</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.30%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col1\" class=\"data row7 col1\" >haskell</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col2\" class=\"data row7 col2\" >18938</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col3\" class=\"data row7 col3\" >17500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col4\" class=\"data row7 col4\" >11500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col5\" class=\"data row7 col5\" >27000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col6\" class=\"data row7 col6\" >40</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col1\" class=\"data row8 col1\" >cpp</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col2\" class=\"data row8 col2\" >18650</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col3\" class=\"data row8 col3\" >17000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col4\" class=\"data row8 col4\" >5250</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col5\" class=\"data row8 col5\" >40000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col6\" class=\"data row8 col6\" >27397</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow8_col7\" class=\"data row8 col7\" >17.64%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col1\" class=\"data row9 col1\" >kotlin</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col2\" class=\"data row9 col2\" >18473</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col3\" class=\"data row9 col3\" >17500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col4\" class=\"data row9 col4\" >7000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col5\" class=\"data row9 col5\" >45000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col6\" class=\"data row9 col6\" >512</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.33%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col1\" class=\"data row10 col1\" >perl</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col2\" class=\"data row10 col2\" >18292</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col3\" class=\"data row10 col3\" >17500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col4\" class=\"data row10 col4\" >5000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col5\" class=\"data row10 col5\" >40000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col6\" class=\"data row10 col6\" >1191</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col1\" class=\"data row11 col1\" >swift</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col2\" class=\"data row11 col2\" >17729</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col3\" class=\"data row11 col3\" >16500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col4\" class=\"data row11 col4\" >6000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col5\" class=\"data row11 col5\" >37943</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col6\" class=\"data row11 col6\" >1241</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.80%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col1\" class=\"data row12 col1\" >julia</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col2\" class=\"data row12 col2\" >17308</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col4\" class=\"data row12 col4\" >12500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col5\" class=\"data row12 col5\" >25000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col6\" class=\"data row12 col6\" >13</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col1\" class=\"data row13 col1\" >java</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col2\" class=\"data row13 col2\" >16176</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col3\" class=\"data row13 col3\" >14583</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col4\" class=\"data row13 col4\" >5250</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col5\" class=\"data row13 col5\" >37500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col6\" class=\"data row13 col6\" >48668</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow13_col7\" class=\"data row13 col7\" >31.34%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col1\" class=\"data row14 col1\" >php</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col2\" class=\"data row14 col2\" >15908</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col3\" class=\"data row14 col3\" >13000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col4\" class=\"data row14 col4\" >4500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col5\" class=\"data row14 col5\" >37500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col6\" class=\"data row14 col6\" >6540</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow14_col7\" class=\"data row14 col7\" >4.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col1\" class=\"data row15 col1\" >objective_c</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col2\" class=\"data row15 col2\" >14791</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col3\" class=\"data row15 col3\" >13312</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col4\" class=\"data row15 col4\" >4600</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col5\" class=\"data row15 col5\" >31333</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col6\" class=\"data row15 col6\" >128</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow15_col7\" class=\"data row15 col7\" >0.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col2\" class=\"data row16 col2\" >14006</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col3\" class=\"data row16 col3\" >12500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col4\" class=\"data row16 col4\" >3750</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col5\" class=\"data row16 col5\" >29166</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col6\" class=\"data row16 col6\" >17457</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow16_col7\" class=\"data row16 col7\" >11.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col2\" class=\"data row17 col2\" >13404</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col3\" class=\"data row17 col3\" >12500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col5\" class=\"data row17 col5\" >27500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col6\" class=\"data row17 col6\" >17128</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow17_col7\" class=\"data row17 col7\" >11.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col1\" class=\"data row18 col1\" >vba</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col2\" class=\"data row18 col2\" >13110</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col3\" class=\"data row18 col3\" >12500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col4\" class=\"data row18 col4\" >5012</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col5\" class=\"data row18 col5\" >27764</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col6\" class=\"data row18 col6\" >201</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow18_col7\" class=\"data row18 col7\" >0.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col1\" class=\"data row19 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col2\" class=\"data row19 col2\" >12958</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col3\" class=\"data row19 col3\" >13000</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col4\" class=\"data row19 col4\" >6250</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col5\" class=\"data row19 col5\" >22500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col6\" class=\"data row19 col6\" >16</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col1\" class=\"data row20 col1\" >delphi</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col2\" class=\"data row20 col2\" >12560</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col3\" class=\"data row20 col3\" >12500</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col4\" class=\"data row20 col4\" >4471</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col5\" class=\"data row20 col5\" >23140</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col6\" class=\"data row20 col6\" >393</td>\n",
       "                        <td id=\"T_04a72ff8_4684_11ea_91b0_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.25%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2571fb15948>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')\n",
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据显示，haskell才是最赚钱的编程语言。python是主流语言里面最赚钱的，比java的工资多了3000元！vb是最不赚钱的了。其中，最赚钱的编程语言和最不赚钱的，工资居然差了2倍。所以，要选好编程语言呀！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 教育"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_counts=data[~(data.edu=='')].groupby(by=['edu']).headcount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edu\n",
       "初中及以下       13\n",
       "博士         253\n",
       "大专       27654\n",
       "本科       63383\n",
       "硕士        4023\n",
       "高中         181\n",
       "Name: headcount, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edu_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(edu_counts.values, labels=edu_counts.index, autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_04d45a86_4684_11ea_bbb0_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >edu</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow0_col0\" class=\"data row0 col0\" >博士</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow0_col1\" class=\"data row0 col1\" >31188</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow0_col2\" class=\"data row0 col2\" >15499</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow0_col3\" class=\"data row0 col3\" >29514</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow0_col4\" class=\"data row0 col4\" >50000</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow0_col5\" class=\"data row0 col5\" >253</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.26%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow1_col0\" class=\"data row1 col0\" >硕士</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow1_col1\" class=\"data row1 col1\" >21814</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow1_col2\" class=\"data row1 col2\" >4000</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow1_col5\" class=\"data row1 col5\" >4023</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow1_col6\" class=\"data row1 col6\" >4.21%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow2_col0\" class=\"data row2 col0\" >本科</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow2_col1\" class=\"data row2 col1\" >17388</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow2_col5\" class=\"data row2 col5\" >63383</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow2_col6\" class=\"data row2 col6\" >66.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow3_col0\" class=\"data row3 col0\" >大专</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow3_col1\" class=\"data row3 col1\" >13758</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow3_col2\" class=\"data row3 col2\" >4239</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow3_col4\" class=\"data row3 col4\" >29022</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow3_col5\" class=\"data row3 col5\" >27654</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow3_col6\" class=\"data row3 col6\" >28.95%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow4_col0\" class=\"data row4 col0\" >初中及以下</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow4_col1\" class=\"data row4 col1\" >10615</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow4_col2\" class=\"data row4 col2\" >9000</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow4_col3\" class=\"data row4 col3\" >9700</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow4_col4\" class=\"data row4 col4\" >12500</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow4_col5\" class=\"data row4 col5\" >13</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow5_col0\" class=\"data row5 col0\" >高中</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow5_col1\" class=\"data row5 col1\" >8573</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow5_col2\" class=\"data row5 col2\" >4503</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow5_col3\" class=\"data row5 col3\" >7000</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow5_col4\" class=\"data row5 col4\" >25450</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow5_col5\" class=\"data row5 col5\" >181</td>\n",
       "                        <td id=\"T_04d45a86_4684_11ea_bbb0_701ce71031efrow5_col6\" class=\"data row5 col6\" >0.19%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2571fb26148>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu=get_sub_stats_by_col(data[data.edu.isin(['初中及以下','高中','大专','本科','硕士','博士'])], 'edu')\n",
    "apply_style(data_edu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_associate=data[data.edu=='大专'].monthly_salary\n",
    "salary_bachelor=data[data.edu=='本科'].monthly_salary\n",
    "salary_master=data[data.edu=='硕士'].monthly_salary\n",
    "salary_phd=data[data.edu=='博士'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大专，本科，硕士，博士的平均工资分别是13566，17531，22235，31783。中位数分别是12500， 16000，20833， 30000。\n"
     ]
    }
   ],
   "source": [
    "print('大专，本科，硕士，博士的平均工资分别是{:.0f}，{:.0f}，{:.0f}，{:.0f}。中位数分别是{:.0f}， {:.0f}，{:.0f}， {:.0f}。'.format(\n",
    "    salary_associate.mean(),salary_bachelor.mean(),salary_master.mean(),salary_phd.mean(),\n",
    "    salary_associate.median(),salary_bachelor.median(),salary_master.median(),salary_phd.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=72.64524591734843, pvalue=3.916962052022129e-17)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_phd, salary_master)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=391.39279239547756, pvalue=2.3856856506118084e-86)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_master, salary_bachelor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=1597.1725790015375, pvalue=0.0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(salary_bachelor, salary_associate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "经过Oneway Anova Test，证明从大专到博士，学历每提高一级，工资都有显著的提高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['大专','本科','硕士','博士']\n",
    "a=sns.boxplot(y='edu',x='monthly_salary',order=order,data=data[data.edu.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(0.5,1.5), xytext=(2, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 工作经验 Working Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_05111568_4684_11ea_921b_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >experience</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow0_col0\" class=\"data row0 col0\" >10+</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow0_col1\" class=\"data row0 col1\" >31563</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow0_col2\" class=\"data row0 col2\" >12391</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow0_col3\" class=\"data row0 col3\" >30000</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow0_col4\" class=\"data row0 col4\" >58389</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow0_col5\" class=\"data row0 col5\" >416</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow0_col6\" class=\"data row0 col6\" >0.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow1_col0\" class=\"data row1 col0\" >5_10</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow1_col1\" class=\"data row1 col1\" >22312</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow1_col2\" class=\"data row1 col2\" >11500</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow1_col3\" class=\"data row1 col3\" >20000</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow1_col5\" class=\"data row1 col5\" >15537</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow1_col6\" class=\"data row1 col6\" >14.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow2_col0\" class=\"data row2 col0\" >3_5</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow2_col1\" class=\"data row2 col1\" >17721</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow2_col2\" class=\"data row2 col2\" >9000</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow2_col5\" class=\"data row2 col5\" >34021</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow2_col6\" class=\"data row2 col6\" >30.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow3_col0\" class=\"data row3 col0\" ></td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow3_col1\" class=\"data row3 col1\" >14654</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow3_col2\" class=\"data row3 col2\" >3500</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow3_col3\" class=\"data row3 col3\" >12500</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow3_col4\" class=\"data row3 col4\" >36636</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow3_col5\" class=\"data row3 col5\" >31521</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow3_col6\" class=\"data row3 col6\" >28.40%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow4_col0\" class=\"data row4 col0\" >1_3</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow4_col1\" class=\"data row4 col1\" >13630</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow4_col2\" class=\"data row4 col2\" >5500</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow4_col3\" class=\"data row4 col3\" >12500</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow4_col5\" class=\"data row4 col5\" >29506</td>\n",
       "                        <td id=\"T_05111568_4684_11ea_921b_701ce71031efrow4_col6\" class=\"data row4 col6\" >26.58%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2571fc0ae08>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_experience=get_sub_stats_by_col(data, 'experience')\n",
    "apply_style(data_experience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_we_10=data[data.experience=='10+'].monthly_salary\n",
    "salary_we_5_10=data[data.experience=='5_10'].monthly_salary\n",
    "salary_we_3_5=data[data.experience=='3_5'].monthly_salary\n",
    "salary_we_1_3=data[data.experience=='1_3'].monthly_salary\n",
    "salary_we_no=data[data.experience=='no'].monthly_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "order=['10+','5_10','3_5','1_3']\n",
    "sns.boxplot(y='experience',x='monthly_salary',order=order,data=data[data.experience.isin(order)], orient='h')\n",
    "plt.annotate('https://github.com/juwikuang/job_survey', xy=(1.5,1.5), xytext=(1.55, 1.55))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 公司 Company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司性质 Company Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_0558f49e_4684_11ea_a682_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_type</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow0_col0\" class=\"data row0 col0\" >外资（欧美）</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow0_col1\" class=\"data row0 col1\" >19287</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow0_col2\" class=\"data row0 col2\" >5400</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow0_col4\" class=\"data row0 col4\" >40000</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow0_col5\" class=\"data row0 col5\" >5976</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow0_col6\" class=\"data row0 col6\" >5.38%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow1_col0\" class=\"data row1 col0\" >外企代表处</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow1_col1\" class=\"data row1 col1\" >17100</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow1_col2\" class=\"data row1 col2\" >7000</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow1_col4\" class=\"data row1 col4\" >22958</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow1_col5\" class=\"data row1 col5\" >30</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow1_col6\" class=\"data row1 col6\" >0.03%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow2_col0\" class=\"data row2 col0\" >合资</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow2_col1\" class=\"data row2 col1\" >16707</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow2_col2\" class=\"data row2 col2\" >5250</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow2_col5\" class=\"data row2 col5\" >9460</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow2_col6\" class=\"data row2 col6\" >8.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow3_col0\" class=\"data row3 col0\" >国企</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow3_col1\" class=\"data row3 col1\" >16369</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow3_col2\" class=\"data row3 col2\" >3750</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow3_col3\" class=\"data row3 col3\" >14583</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow3_col4\" class=\"data row3 col4\" >45000</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow3_col5\" class=\"data row3 col5\" >7279</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow3_col6\" class=\"data row3 col6\" >6.56%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow4_col0\" class=\"data row4 col0\" >民营公司</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow4_col1\" class=\"data row4 col1\" >16287</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow4_col2\" class=\"data row4 col2\" >5000</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow4_col3\" class=\"data row4 col3\" >14500</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow4_col4\" class=\"data row4 col4\" >37500</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow4_col5\" class=\"data row4 col5\" >81054</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow4_col6\" class=\"data row4 col6\" >73.02%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow5_col0\" class=\"data row5 col0\" >外资（非欧美）</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow5_col1\" class=\"data row5 col1\" >15907</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow5_col3\" class=\"data row5 col3\" >14000</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow5_col4\" class=\"data row5 col4\" >37500</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow5_col5\" class=\"data row5 col5\" >6563</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow5_col6\" class=\"data row5 col6\" >5.91%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow6_col0\" class=\"data row6 col0\" >非营利组织</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow6_col1\" class=\"data row6 col1\" >15139</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow6_col2\" class=\"data row6 col2\" >6250</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow6_col3\" class=\"data row6 col3\" >12583</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow6_col4\" class=\"data row6 col4\" >36667</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow6_col5\" class=\"data row6 col5\" >100</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow6_col6\" class=\"data row6 col6\" >0.09%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow7_col0\" class=\"data row7 col0\" >事业单位</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow7_col1\" class=\"data row7 col1\" >14417</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow7_col2\" class=\"data row7 col2\" >5250</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow7_col3\" class=\"data row7 col3\" >12999</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow7_col4\" class=\"data row7 col4\" >35000</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow7_col5\" class=\"data row7 col5\" >539</td>\n",
       "                        <td id=\"T_0558f49e_4684_11ea_a682_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.49%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2571fd58f08>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_type=get_sub_stats_by_col(data,'company_type')\n",
    "apply_style(data_company_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "us_eu是欧美外企，startup是创业公司，listed是上市公司，state是国企，private是私企，foreign是非欧美外企，其他不足1000个样本的不管了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 公司规模 Company Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_05694874_4684_11ea_9f4a_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >company_size</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow0_col0\" class=\"data row0 col0\" >10000+</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow0_col1\" class=\"data row0 col1\" >24906</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow0_col2\" class=\"data row0 col2\" >7000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow0_col3\" class=\"data row0 col3\" >25000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow0_col4\" class=\"data row0 col4\" >48000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow0_col5\" class=\"data row0 col5\" >7602</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow0_col6\" class=\"data row0 col6\" >6.85%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow1_col0\" class=\"data row1 col0\" >5000-10000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow1_col1\" class=\"data row1 col1\" >19311</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow1_col2\" class=\"data row1 col2\" >8000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow1_col4\" class=\"data row1 col4\" >45000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow1_col5\" class=\"data row1 col5\" >2973</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow1_col6\" class=\"data row1 col6\" >2.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow2_col0\" class=\"data row2 col0\" ></td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow2_col1\" class=\"data row2 col1\" >17797</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow2_col2\" class=\"data row2 col2\" >7000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow2_col4\" class=\"data row2 col4\" >37500</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow2_col5\" class=\"data row2 col5\" >581</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow2_col6\" class=\"data row2 col6\" >0.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow3_col0\" class=\"data row3 col0\" >1000-5000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow3_col1\" class=\"data row3 col1\" >16768</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow3_col2\" class=\"data row3 col2\" >6000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow3_col4\" class=\"data row3 col4\" >37500</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow3_col5\" class=\"data row3 col5\" >13980</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow3_col6\" class=\"data row3 col6\" >12.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow4_col0\" class=\"data row4 col0\" >500-1000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow4_col1\" class=\"data row4 col1\" >16716</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow4_col2\" class=\"data row4 col2\" >4400</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow4_col4\" class=\"data row4 col4\" >35000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow4_col5\" class=\"data row4 col5\" >10836</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow4_col6\" class=\"data row4 col6\" >9.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow5_col0\" class=\"data row5 col0\" >150-500</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow5_col1\" class=\"data row5 col1\" >16121</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow5_col3\" class=\"data row5 col3\" >15000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow5_col4\" class=\"data row5 col4\" >35000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow5_col5\" class=\"data row5 col5\" >25996</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow5_col6\" class=\"data row5 col6\" >23.42%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow6_col0\" class=\"data row6 col0\" >50-150</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow6_col1\" class=\"data row6 col1\" >15302</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow6_col2\" class=\"data row6 col2\" >4500</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow6_col3\" class=\"data row6 col3\" >14000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow6_col4\" class=\"data row6 col4\" >35000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow6_col5\" class=\"data row6 col5\" >34050</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow6_col6\" class=\"data row6 col6\" >30.68%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow7_col0\" class=\"data row7 col0\" >50-</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow7_col1\" class=\"data row7 col1\" >14283</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow7_col2\" class=\"data row7 col2\" >4000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow7_col3\" class=\"data row7 col3\" >12500</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow7_col4\" class=\"data row7 col4\" >35000</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow7_col5\" class=\"data row7 col5\" >14983</td>\n",
       "                        <td id=\"T_05694874_4684_11ea_9f4a_701ce71031efrow7_col6\" class=\"data row7 col6\" >13.50%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x257204d5ec8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_company_size=get_sub_stats_by_col(data,'company_size')\n",
    "apply_style(data_company_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "公司越大，工资越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业 Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_057e0754_4684_11ea_95d0_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >industry</th>        <th class=\"col_heading level0 col1\" >salary_mean</th>        <th class=\"col_heading level0 col2\" >salary_95_min</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_max</th>        <th class=\"col_heading level0 col5\" >head_count</th>        <th class=\"col_heading level0 col6\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow0_col0\" class=\"data row0 col0\" >logistic</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow0_col1\" class=\"data row0 col1\" >20323</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow0_col2\" class=\"data row0 col2\" >5579</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow0_col3\" class=\"data row0 col3\" >17500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow0_col4\" class=\"data row0 col4\" >48000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow0_col5\" class=\"data row0 col5\" >1963</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow0_col6\" class=\"data row0 col6\" >1.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow1_col0\" class=\"data row1 col0\" >finance</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow1_col1\" class=\"data row1 col1\" >18129</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow1_col2\" class=\"data row1 col2\" >5465</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow1_col4\" class=\"data row1 col4\" >37286</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow1_col5\" class=\"data row1 col5\" >3912</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow1_col6\" class=\"data row1 col6\" >3.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow2_col0\" class=\"data row2 col0\" >edu</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow2_col1\" class=\"data row2 col1\" >16617</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow2_col2\" class=\"data row2 col2\" >6500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow2_col3\" class=\"data row2 col3\" >15000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow2_col4\" class=\"data row2 col4\" >40000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow2_col5\" class=\"data row2 col5\" >6282</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow2_col6\" class=\"data row2 col6\" >5.66%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow3_col0\" class=\"data row3 col0\" >computer</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow3_col1\" class=\"data row3 col1\" >16515</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow3_col2\" class=\"data row3 col2\" >5000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow3_col3\" class=\"data row3 col3\" >15000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow3_col4\" class=\"data row3 col4\" >37500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow3_col5\" class=\"data row3 col5\" >84651</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow3_col6\" class=\"data row3 col6\" >76.26%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow4_col0\" class=\"data row4 col0\" >service</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow4_col1\" class=\"data row4 col1\" >15411</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow4_col2\" class=\"data row4 col2\" >5600</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow4_col4\" class=\"data row4 col4\" >30000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow4_col5\" class=\"data row4 col5\" >581</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow4_col6\" class=\"data row4 col6\" >0.52%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow5_col0\" class=\"data row5 col0\" >trade</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow5_col1\" class=\"data row5 col1\" >15339</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow5_col2\" class=\"data row5 col2\" >5250</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow5_col3\" class=\"data row5 col3\" >14000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow5_col4\" class=\"data row5 col4\" >32500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow5_col5\" class=\"data row5 col5\" >6201</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow5_col6\" class=\"data row5 col6\" >5.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow6_col0\" class=\"data row6 col0\" >ads</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow6_col1\" class=\"data row6 col1\" >15179</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow6_col2\" class=\"data row6 col2\" >5890</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow6_col3\" class=\"data row6 col3\" >13381</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow6_col4\" class=\"data row6 col4\" >35000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow6_col5\" class=\"data row6 col5\" >1228</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow6_col6\" class=\"data row6 col6\" >1.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow7_col0\" class=\"data row7 col0\" >energy</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow7_col1\" class=\"data row7 col1\" >14960</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow7_col2\" class=\"data row7 col2\" >3750</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow7_col3\" class=\"data row7 col3\" >15000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow7_col4\" class=\"data row7 col4\" >30000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow7_col5\" class=\"data row7 col5\" >1058</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow7_col6\" class=\"data row7 col6\" >0.95%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow8_col0\" class=\"data row8 col0\" >medical</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow8_col1\" class=\"data row8 col1\" >14715</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow8_col2\" class=\"data row8 col2\" >4500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow8_col3\" class=\"data row8 col3\" >14000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow8_col4\" class=\"data row8 col4\" >30000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow8_col5\" class=\"data row8 col5\" >2328</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow8_col6\" class=\"data row8 col6\" >2.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow9_col0\" class=\"data row9 col0\" >gov</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow9_col1\" class=\"data row9 col1\" >14642</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow9_col2\" class=\"data row9 col2\" >2918</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow9_col3\" class=\"data row9 col3\" >12500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow9_col4\" class=\"data row9 col4\" >32562</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow9_col5\" class=\"data row9 col5\" >1251</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow9_col6\" class=\"data row9 col6\" >1.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow10_col0\" class=\"data row10 col0\" >realestate</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow10_col1\" class=\"data row10 col1\" >14479</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow10_col2\" class=\"data row10 col2\" >3750</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow10_col3\" class=\"data row10 col3\" >12500</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow10_col4\" class=\"data row10 col4\" >30000</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow10_col5\" class=\"data row10 col5\" >1546</td>\n",
       "                        <td id=\"T_057e0754_4684_11ea_95d0_701ce71031efrow10_col6\" class=\"data row10 col6\" >1.39%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25720595188>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_industry=get_sub_stats_by_col(data,'industry')\n",
    "apply_style(data_industry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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